Content-based information retrieval (CBIR) attempts to solve the problem of searching for digital images in large databases. One of the concerns in CBIR literature is about how to rank the results of these similarity searches, and how to visualize that ranking There exist many different ways in which a digital image can be “similar” to a query image. A CBIR system should make the user aware of the similarity definition and how to interpret the relationship between the retrieved images and the query. Typically, the retrieved images are ranked based on their similarity to the query image. The system is arranged to show the retrieved images to the user in a sequential order, the more similar images preceding those less similar. Several examples of CBIR systems are described in a review article by R. Datta et al entitled “Image retrieval, ideas, influences and trends of the new age”, published in ACM Computing Surveys 40, No. 2 pp. 5:1-60, 2008.
A drawback of the existing CBIR methods is that the user is often not able to see why the retrieved images are similar or dissimilar to the query image. In particular, it is often not obvious whether a suspicious portion of interest of the query image is similar to the respective portion of the retrieved image. It also difficult to compare and evaluate retrieved images with respect to each other.